Airfoil design generally involves high dimensionality and nonlinear mappings, leaving the preferred infill sampling strategies in the framework of Bayesian optimization uncertain across instances and changeable in the sequential optimization stages. Knottily, no single-infill sampler guarantees the optimal results for airfoil design for its changeless intrinsic selection mechanism, whereas parallel strategies consume multifold computing resources for high-fidelity simulations. Therefore, this paper proposes an airfoil optimization framework using portfolios that integrates several single-infill samplers and assimilates a suggestion from them under the instruction of a meta-criterion. Two entropy-based portfolios, entropy search portfolio and max-value entropy search portfolio, are used as illustration for alternative scenarios. Comparative results between the proposed framework and frameworks using individual constituent samplers of the portfolios on both aerodynamic design and aerodynamic-stealth design show that the portfolio-based framework automatically authorizes the most suitable constituent at each iteration and guides fast convergences to optimal airfoils of high confidence without being influenced by underperforming constituents across instances of different complexity. It is demonstrated that the proposed portfolio-based airfoil optimization framework is robust and reliable for airfoil design instances without priors for options.

机翼设计通常涉及高维和非线性映射，从而使贝叶斯优化框架中的首选填充采样策略在各个实例之间不确定，并且在顺序优化阶段可以更改。值得一提的是，没有任何单一填充采样器能够通过其不变的内在选择机制来保证机翼设计的最佳结果，而并行策略则消耗了用于高保真模拟的多种计算资源。因此，本文提出了一种使用投资组合的机翼优化框架，该框架集成了多个单填充采样器，并在元准则的指导下吸收了他们的建议。两种基于熵的组合，熵搜索组合和最大值熵搜索组合用作替代方案的说明。所提议的框架与使用空气动力学设计和空气动力学隐身设计的投资组合的各个成分采样器的框架之间的比较结果表明，基于投资组合的框架在每次迭代时自动授权最合适的成分，并指导快速收敛至高置信度的最佳翼型不受不同复杂度情况下表现不佳的成分的影响。事实证明，所提出的基于投资组合的机翼优化框架对于机翼设计实例是稳健而可靠的，而无需事先选择。